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<title>Multi-view Dynamic Human Datasets</title>
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<h2 id="title" class="auto-style1">
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Multi-view Dynamic Human Datasets</h2>

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	<a href="https://github.com/wuminye" target="_blank">Minye Wu</a><sup>1,3,4</sup>
	&nbsp;&nbsp;&nbsp;
	<a href="https://github.com/yuehaowang" target="_blank">Yuehao Wang</a><sup>1</sup>
	&nbsp;&nbsp;&nbsp;
	Qiang Hu<sup>1</sup>
	&nbsp;&nbsp;&nbsp;
	Jingyi Yu<sup>1,2</sup>
	&nbsp;&nbsp;&nbsp;
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<p class="auto-style7"  align="center">
<sup>1</sup> ShanghaiTech University
&nbsp;&nbsp;&nbsp;
<sup>2</sup> DGene Inc.
&nbsp;&nbsp;&nbsp;
<sup>3</sup> University of Chinese Academy of Sciences
&nbsp;&nbsp;&nbsp;<br>
<sup>4</sup> Shanghai Institute of Microsystem and Information Technology</p>
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	<td><p class="auto-style5"> In this dataset, five sequences are collected by a multi-camera dome system with up to 80 cameras arranged on a cylinder. 
	 All cameras are synchronized, calibrated and capture at 25 frames per second. Performers are in different clothing and perform
different actions. All sequences have a length between 8 to
24 seconds. Specifically `sport1`, `sport2`, `sport3` correspond
to dumbbell lifting with relatively tight clothing, `dance` contains complex and highly deformable clothing, 
and `basketball` involves interactions between a player and a ball.

	</p>
	<p class="auto-style5"> 
	Each sequence contains RGB images, forground masks, RGB point cloud sequence and camera calibration.  We
use chrome key based matting followed by manual fixing to extract the ground truth masks for all views. We use one of the
best commercial SfM software Metashape to compute the initial 3D point clouds for all frames.
	 

	</p>

	<p class="auto-style5"> 
	  Datasets are now released for non-commercial purposes. Please see <a href='#downloads'>here</a> <br>
	  File structure is describled in <a href='https://github.com/wuminye/NHR/blob/master/README.md'>here</a>
    </p>
	
	
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<p class="auto-style5">&nbsp;</p>
<p id="results", class="auto-style4"><strong>Results</strong></p>
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	Video below shows results on the 5 datasets. Please see <a href='index.html'>here</a> for more details. 
</p>
<div>
<iframe width="640" height="360" src="https://www.youtube.com/embed/PZyPFOKIsXA" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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<p class="auto-style5">&nbsp;</p>
<p id="downloads", class="auto-style4"><strong>Citation</strong></p>
<p>
<pre  class="auto-style5" style="background: #EEEEEE; padding: 20px; font-family: 'Courier New', Courier, monospace;">
@inproceedings{wu2020multi,
   title={Multi-View Neural Human Rendering},
   author={Wu, Minye and Wang, Yuehao and Hu, Qiang and Yu, Jingyi},
   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
   pages={1682--1691},
   year={2020}
}
</pre>
</p>

<p class="auto-style5">&nbsp;</p>
<p id="downloads", class="auto-style4"><strong>Downloads</strong></p>

<table cellSpacing=4 cellPadding=2 border=0 style="width: 90%">
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	<td align="center" valign="center">
		<img style="padding:0; clear:both; " src="figures/paper_icon.png" align="middle" alt="Snapshot for paper" class="pdf" width="200" />
	</td>
	<td align="left" class="auto-style5">&quot; Multi-view Neural Human Rendering &quot;<br>
Minye Wu, Yuehao Wang, Qiang Hu, Jingyi Yu.<br>
<i>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020</i><br><br>
<img alt="" height="32" src="figures/pdf_icon.gif" width="31">&nbsp;&nbsp;[<a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Wu_Multi-View_Neural_Human_Rendering_CVPR_2020_paper.pdf">Paper (pdf, 2.1MB)</a>]&nbsp;&nbsp;

<img alt="" height="32" src="figures/ppt.gif" width="31">&nbsp;&nbsp;[<a href="https://drive.google.com/file/d/1btIXS6hFKcRLHOSPzbjrG0ysJXoMExHf/view">Poster Slides (pdf, 1.3MB)</a>]<br><br> 

<img alt="" height="32" src="figures/code_icon.png" width="31">&nbsp;&nbsp;[<a href="https://github.com/wuminye/NHR">Code (github)</a>]<br><br> 

<img alt="" height="32" width="32" src="figures/file_icon.png">&nbsp;
[Datasets
	<a href="https://drive.google.com/file/d/1BnqbXXzswcDh_3VzkYz1PW6LMWVf_HNv/view?usp=sharing">sport_1(7z, 2.4GB)</a>&nbsp;
	<a href="https://drive.google.com/file/d/1MkckRKXKtYbmg60lwKCBG3oqLqFqDJay/view?usp=sharing">sport_2(7z, 2.7GB)</a>&nbsp;
	<a href="https://drive.google.com/file/d/1geWcvWJGMlbCOtYHq1lAHI7RfAjFQ4_6/view?usp=sharing">sport_3(7z, 2.7GB)</a>&nbsp;
	<a href="https://drive.google.com/file/d/1WbLYWFJZ0kRtzsPZKn6Unibe0Je9wm8r/view?usp=sharing">basketball(7z, 4.5GB)</a>&nbsp;
	<a href="https://drive.google.com/file/d/1gtS6C4uCI_YV_AJP6xLn_dGUGhMitL9T/view?usp=sharing">dance(7z, 8.6GB)</a>]<br><br>
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<p class="auto-style1" style="color: #999999">Last update: Sep. 26, 2020</p>


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